I was wondering what's the best way to get a mapping between Wikidata QIDs
and Google Knowledge Graph (ex-Freebase) MIDs.
We tried to extract the mapping from Wikidata, using the property
https://www.wikidata.org/wiki/Property:P646, among others, but it doesn't
seem complete, with mappings for only 1.3 million entities -- much less
than the number of Wikidata entities.
Is there maybe a dedicated dataset or API for this mapping?
Alternatively, does anyone know of a way of querying the Google KG API 
directly with the name of a Wikipedia article (or with a Wikidata QID),
rather than an arbitrary plain-text string?
The May 2020 issue of the Wikimedia Research Newsletter is out:
In this issue:
1 Automatic detection of undisclosed paid editing2 Wikiworkshop 20202.1 "A Deeper Investigation of the Importance of Wikipedia Links to the Success of Search Engines"2.2 "Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph"2.3 "WikiHist.html: English Wikipedia's Full Revision History in HTML Format"2.4 "Collaboration of Open Content News in Wikipedia: The Role and Impact of Gatekeepers"2.5 "Domain-Specific Automatic Scholar Profiling Based on Wikipedia"2.6 "Matching Ukrainian Wikipedia Red Links with English Wikipedia’s Articles"2.7 "Beyond Performing Arts: Network Composition and Collaboration Patterns"2.8 "Content Growth and Attention Contagion in Information Networks: Addressing Information Poverty on Wikipedia"2.9 "The Positioning Matters: Estimating Geographical Bias in the Multilingual Record of Biographies on Wikipedia"2.10 "Citation Detective: a Public Dataset to Improve and Quantify Wikipedia Citation Quality at Scale"3 Briefly
- *** 12 recent publications were covered or listed in this issue ***
Masssly and Tilman Bayer
Wikimedia Research Newsletter
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* Receive this newsletter by mail: Research-newsletter Mailing List - Wikimedia
As several Wikipedia researchers are working toward understanding
Wikipedia's role during this pandemic, I thought I'd share our recent paper
"Sudden Attention Shifts on Wikipedia Following COVID-19 Mobility
Stay safe, everyone on this list!
Manoel Horta Ribeiro, Kristina Gligorić, Maxime Peyrard, Florian Lemmerich,
Markus Strohmaier, Robert West
We study how the coronavirus disease 2019 (COVID-19) pandemic, alongside
the severe mobility restrictions that ensued, has impacted information
access on Wikipedia, the world's largest online encyclopedia. A
longitudinal analysis that combines pageview statistics for 12 Wikipedia
language editions with mobility reports published by Apple and Google
reveals a massive increase in access volume, accompanied by a stark shift
in topical interests. Health- and entertainment- related topics are found
to have gained, and sports- and transportation- related topics, to have
lost attention. Interestingly, while the interest in health-related topics
was transient, that in entertainment topics is lingering and even
increasing. These changes began at the time when mobility was restricted
and are most pronounced for language editions associated with countries, in
which the most severe mobility restrictions were implemented, indicating
that the interest shift might be caused by people's spending more time at
home. Our results highlight the utility of Wikipedia for studying reactions
to the pandemic across the globe, and illustrate how the disease is
rippling through society.